Advanced Graph Neural Networks

Derniers papiers

• GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs

  Hengyi Feng, Zeang Sheng, Meiyi Qiang, Meiyi Qiang, Wentao Zhang · 11 juin 2026

  Research on Text-Attributed Graphs (TAGs) has gained significant attention recently due to its broad applications across various real-world data scenarios, such as citation networks, e-commerce platforms, social media, and web pages. Inspired by the remarkable semantic understanding ability of Large…

• The ASE-LSE Disagreement Landscape: An End-to-End Characterisation of Extremes and Structural Drivers

  Minh Triet Pham, Ian Gallagher · 11 juin 2026

  Two of the most widely used methods for analysing graph data, Adjacency Spectral Embedding and Laplacian Spectral Embedding, often produce different results when applied to the same graph. Yet the structural reasons behind this disagreement remain incompletely understood. This paper provides an end-…

• GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs

  Zhuoyi Peng, Jingzhou Jiang, Hanlin Gu, Lixin Fan, Yi Yang · 11 juin 2026

  Graph analysis underlies many applications whose answers cannot be looked up in a single record or retrieved along a path: laundering rings, drug repurposing, user preference, and scientific theme are all inferred from a node together with its neighbourhood. We introduce GraphInfer-Bench, a benchmar…

• Hubs or Fringes: Pretraining Data Selection via Web Graph Centrality

  Vedant Badoni, Danqi Chen, Xinyi Wang · 11 juin 2026

  The performance of modern language models depends critically on pretraining data composition. Yet existing data selection methods rely on auxiliary classifiers for document scoring or mixture optimization, adding computational overhead and dependence on labeled data. We propose WebGraphMix, a lightw…

• LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems

  Arijit Khan, Longxu Sun, Xin Huang · 11 juin 2026

  Large Language Models (LLMs) have advanced rapidly, but their limitations in structured and multi-hop reasoning underscore the need for graph-native, synergistic artificial intelligence (AI) systems. Graph-structured data underpins critical applications across social, biological, financial, transpor…

• Neuro-Relational Programs: Unifying Queries and Neural Computation over Structured Data

  Arie Soeteman, Balder ten Cate, Maurice Funk, Benny Kimelfeld, Carsten Lutz, Moritz Sch\"onherr · 11 juin 2026

  The conventional approach to deep learning over relational databases applies neural models, such as Graph Neural Networks (GNNs), to a graph representation of the database. Recent approaches instead operate on databases directly, associating tuples with embeddings and extending query mechanisms to j…

• Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching

  Zhuoyi Peng, Hanlin Gu, Lixin Fan, Yi Yang · 11 juin 2026

  Text-attributed graphs (TAGs) underlie real-world applications such as citation networks, social media, and e-commerce. Few-shot graph learning on TAGs is hard: with only a handful of labels per class and the rest of the graph unannotated, neither GNNs nor LLMs can learn well on their own. GNNs read…

• From Uniform to Learned Graph Priors: Diffusion for Structure Discovery

  Qi Shao, Hao Guo, Jiawen Chen, Duxin Chen, Wenwu Yu · 11 juin 2026

  Neural relational inference (NRI) methods discover interaction graphs from trajectories through variational reasoning on discrete potential edges. However, these methods typically rely on oversimplified, factorized graph priors. Such priors, typically nearing uniform distributions, treat edges as in…

• Probabilistic Salary Prediction with Graph Attention Networks and a Mixture Density Network

  Zhipei Qin, Mohammad Shokri, N. van Weeren, F. W. Takes · 11 juin 2026

  Accurate salary prediction is critical for bridging the information gap between employers and job seekers in modern labor markets. Existing approaches predominantly yield a single point estimate and treat job attributes such as location, occupation, and industry as independent categorical features, …

• Temporal Sheaf Neural Networks with Dynamic Orthogonal Transport

  Md Sadek Hossain Asif, Tanzila Khan, Md. Mosaddek Khan · 10 juin 2026

  We introduce Temporal Sheaf Neural Networks (TSNN), a temporal link prediction framework that equips each node with a time-varying orthogonal frame and compares node states only after explicit transport between local coordinate systems. In contrast to existing continuous-time graph models that opera…

• Non-Parametric Structural Priors for Geometry Theorem Prediction

  Junbo Zhao, Ting Zhang, Can Li, Wei He, Jingdong Wang, Hua Huang · 10 juin 2026

  Multi-step theorem prediction is a central challenge in geometry problem solving. Existing neural-symbolic approaches rely heavily on supervised parametric models, which exhibit limited generalization to evolving theorem libraries. In this work, we explore training-free theorem prediction through th…

• PADD: Path-Aligned Decompression Distillation for Non-Router Teacher to Guide MoE Student Learning

  Xinyue Peng, Yi Qian, Jiaojiao Lin, Wenjian Shao, Yanming Liu · 10 juin 2026

  As large language models (LLMs) continue to scale, it becomes increasingly challenging to grow model capacity under fixed computation budgets. We propose Path-Aligned Decompression Distillation (PADD), a framework for distilling knowledge from dense teachers without explicit routing into mixture-of-…

• COGENT: Continuous Graph Emulators with Neural Ordinary Differential Equations for Long-Term Physical Forecasting

  Zesheng Liu, Maryam Rahnemoonfar · 10 juin 2026

  In this work, we present COGENT, a continuous graph emulator with Neural Ordinary Differential Equations for long-term physical forecasting on irregular geospatial meshes. COGENT encodes a finite history of system states and associated forcing fields and external forcings with a graph-based history …

• ERAlign: Energy-based Representation Alignment of GNNs and LLMs on Text-attributed Graphs

  Xianlin Zeng, Fan Xia, Xiangyu Chen · 10 juin 2026

  Text-attributed Graphs (TAGs) incorporate textual node attributes with graph structures to describe rich relational semantics. Recent efforts to integrate Graph Neural Networks (GNNs) and Large Language Models (LLMs) have shown promise for learning on TAGs, yet achieving well-aligned representations…

• Sampling Triangulations and Calabi-Yau Threefolds with Autoregressive GNNs

  Nate MacFadden · 10 juin 2026

  We introduce `dualGNN', an autoregressive message-passing GNN for sampling fine, regular triangulations (FRTs) of convex polytopes. dualGNN operates on a generalization of the dual graph of a triangulation, with edges labeled by `signed circuits' -- combinatorial invariants from oriented matroid the…

• When Design Rules Break: Benchmark Composition Determines Whether Label Informativeness Predicts GNN Aggregator Choice

  Neha Sharma, Ritesh Sharma · 10 juin 2026

  We examine whether graph neural network (GNN) design rules generalize across benchmark families by studying aggregator selection (sum, mean, max) on 24 node-classification datasets spanning citation, heterophilic, LINKX Facebook-100, co-purchase, and co-authorship graphs. Edge homophily is only weak…

• Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing

  Lianze Shan, Ningchong Wang, Jitao Zhao, Di Jin, Dongxiao He · 10 juin 2026

  Graph Contrastive Learning (GCL), which trains graph encoders by maximizing similarity between positive samples and minimizing it between negative ones, has emerged as a mainstream graph pre-training paradigm. It is widely recognized that positive samples are essential in GCLs. Ideally, maximizing t…

• When Metrics Disagree: A Meta-Analysis of Knowledge-Graph-Completion Model Benchmarking

  Haji Gul, Ajaz Ahmad Bhat · 10 juin 2026

  Evaluating Knowledge Graph Completion (KGC) models remains challenging because standard assessment relies on isolated rank-based metrics such as MRR, Hits$@$k, and Mean Rank, which often produce conflicting model orderings across datasets. A model that leads on MRR may trail on Hits@1, and strong pe…

• Beyond Convolution: Advancing Hypergraph Neural Networks with Hypergraph U-Nets

  Fuli Wang, Wei Qian, Daniel L. Lau, Gonzalo R. Arce · 9 juin 2026

  Convolutions have successfully transitioned from image processing to the complex realm of non-Euclidean higher-order domains, particularly in hypergraphs. Despite the success in convolution, the exploration of a popular architecture named U-Net remains largely unexplored for hypergraph data due to t…

• Graph Neural Networks for Predicting Solvability of Finite Groups

  Tal Weissblat · 9 juin 2026

  We present a Graph Neural Network (GNN) framework for the classification of finite groups according to their solvability. Using graph representations associated with finite groups, including Cayley graphs (CG), the proposed model is trained to distinguish solvable and non-solvable groups using struc…

• Heterophily-Aware Adaptive Knowledge Distillation for Hypergraph Neural Networks

  Joohee Cho, David Yoon Suk Kang, Yunyong Ko · 9 juin 2026

  Hypergraph knowledge distillation aims to retain the predictive performance of a hypergraph neural network (HNN) teacher while reducing inference costs through a lightweight student model. In this work, we observe that HNNs exhibit substantially lower prediction performance on heterophilic nodes con…

• PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning

  Zekai Chen, Miao Zhang, Jiayang Xing, Xunkai Li, Xun Wu, Rong-Hua Li, Guoren Wang · 9 juin 2026

  Multimodal federated graph learning (MM-FGL) aims to collaboratively learn from decentralized graphs with text and images. However, real-world clients may not share a common modality basis: a visual-search client may contain image--interaction graphs but no seller descriptions, while a catalog clien…

• Implicit Causal Graph Construction in Text via Chain Discovery

  Liesbeth Allein, Marie-Francine Moens · 9 juin 2026

  Causal graphs in text are typically populated by observable, predefined events. In contrast, we study implicit causal graph construction from text by treating each described cause-effect pair as the begin- and endpoint of an underlying latent causal graph and using large language models (LLMs) to in…

• Characterizing the Discrete Geometry of ReLU Networks

  Blake B. Gaines, Jinbo Bi · 9 juin 2026

  It is well established that ReLU networks define continuous piecewise-linear functions, and that their linear regions are polyhedra in the input space. These regions form a complex that fully partitions the input space. The way these regions fit together is fundamental to the behavior of the network…

• What Makes a Desired Graph for Relational Deep Learning?

  Yao Cheng, Siqiang Luo · 9 juin 2026

  Relational deep learning (RDL) converts relational databases (RDBs) into heterogeneous graphs, but graphs derived directly from database schemas are often not well suited for how graph neural networks (GNNs) perform relational reasoning. We study what makes a relational graph suitable for deep learn…